The Experts below are selected from a list of 15945 Experts worldwide ranked by ideXlab platform
Jean-philippe Vert - One of the best experts on this subject based on the ideXlab platform.
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A convex formulation for joint RNA Isoform detection and quantification from multiple RNA-seq samples
BMC Bioinformatics, 2015Co-Authors: Elsa Bernard, Laurent Jacob, Julien Mairal, Eric Viara, Jean-philippe VertAbstract:Detecting and quantifying Isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. We propose a new method for solving the Isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. Our convex formulation to jointly detect and quantify Isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some Isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop .
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A convex formulation for joint RNA Isoform detection and quantification from multiple RNA-seq samples
BMC Bioinformatics, 2015Co-Authors: Elsa Bernard, Laurent Jacob, Julien Mairal, Eric Viara, Jean-philippe VertAbstract:Background Detecting and quantifying Isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. Results We propose a new method for solving the Isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. Conclusion Our convex formulation to jointly detect and quantify Isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some Isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop .
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Efficient RNA Isoform identification and quantification from RNA-Seq data with network flows.
Bioinformatics, 2014Co-Authors: Elsa Bernard, Laurent Jacob, Julien Mairal, Jean-philippe VertAbstract:Several state-of-the-art methods for Isoform identification and quantification are based on l1- regularized regression, such as the Lasso. However, explicitly listing the--possibly exponentially-- large set of candidate transcripts is intractable for genes with many exons. For this reason, existing approaches using the l1-penalty are either restricted to genes with few exons, or only run the regression algorithm on a small set of pre-selected Isoforms. We introduce a new technique called FlipFlop which can efficiently tackle the sparse estimation problem on the full set of candidate Isoforms by using network flow optimization. Our technique removes the need of a preselection step, leading to better Isoform identification while keeping a low computational cost. Experiments with synthetic and real RNA-Seq data confirm that our approach is more accurate than alteRNAtive methods and one of the fastest available. Source code is freely available as an R package from the Bioconductor web site (http://www.bioconductor.org/) and more information is available at http://cbio.ensmp.fr/flipflop.
Wei Sun - One of the best experts on this subject based on the ideXlab platform.
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isodot detects differential RNA Isoform expression usage with respect to a categorical or continuous covariate with high sensitivity and specificity
Journal of the American Statistical Association, 2015Co-Authors: Wei Sun, Yufeng Liu, James J. Crowley, Ting Huei Chen, Hua Zhou, Haitao Chu, Shunping Huang, Pei Fen Kuan, Darla R. Miller, Ginger D ShawAbstract:We have developed a statistical method named IsoDOT to assess differential Isoform expression (DIE) and differential Isoform usage (DIU) using RNA-seq data. Here Isoform usage refers to relative Isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation in practice, for example, comparing the pateRNAl and mateRNAl alleles of one individual or comparing tumor and normal samples of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on the mouse transcriptome and identify a group of genes whose Isoform usages respond to haloperidol treatment. Supplementary materials for this article are available online.
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IsoDOT Detects Differential RNA-Isoform Expression/Usage With Respect to a Categorical or Continuous Covariate With High Sensitivity and Specificity
Journal of the American Statistical Association, 2015Co-Authors: Wei Sun, Yufeng Liu, James J. Crowley, Ting Huei Chen, Hua Zhou, Haitao Chu, Shunping Huang, Pei Fen Kuan, Darla R. MillerAbstract:We have developed a statistical method named IsoDOT to assess differential Isoform expression (DIE) and differential Isoform usage (DIU) using RNA-seq data. Here Isoform usage refers to relative Isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation in practice, for example, comparing the pateRNAl and mateRNAl alleles of one individual or comparing tumor and normal samples of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on the mouse transcriptome and identify a group of genes whose Isoform usages respond to haloperidol treatment. Supplementary materials for this article are available online.
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IsoDOT Detects Differential RNA-Isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity
arXiv: Applications, 2014Co-Authors: Wei Sun, Yufeng Liu, James J. Crowley, Ting Huei Chen, Hua Zhou, Haitao Chu, Shunping Huang, Pei Fen Kuan, Darla R. MillerAbstract:We have developed a statistical method named IsoDOT to assess differential Isoform expression (DIE) and differential Isoform usage (DIU) using RNA-seq data. Here Isoform usage refers to relative Isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation in practice, e.g., comparing pateRNAl and mateRNAl allele of one individual or comparing tumor and normal sample of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on mouse transcriptome and identify a group of genes whose Isoform usages respond to haloperidol treatment.
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isodot detects differential RNA Isoform expression usage with respect to a categorical or continuous covariate with high sensitivity and specificity
arXiv: Applications, 2014Co-Authors: Wei Sun, Yufeng Liu, James J. Crowley, Ting Huei Chen, Hua Zhou, Haitao Chu, Shunping Huang, Pei Fen Kuan, Darla R. Miller, Ginger D ShawAbstract:We have developed a statistical method named IsoDOT to assess differential Isoform expression (DIE) and differential Isoform usage (DIU) using RNA-seq data. Here Isoform usage refers to relative Isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation in practice, e.g., comparing pateRNAl and mateRNAl allele of one individual or comparing tumor and normal sample of one cancer patient. Simulation studies demonstrate the high sensitivity and specificity of IsoDOT. We apply IsoDOT to study the effects of haloperidol treatment on mouse transcriptome and identify a group of genes whose Isoform usages respond to haloperidol treatment.
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eQTL Mapping Using RNA-seq Data
Statistics in Biosciences, 2013Co-Authors: Wei SunAbstract:As RNA-seq is replacing gene expression microarrays to assess genome-wide transcription abundance, gene expression Quantitative Trait Locus (eQTL) studies using RNA-seq have emerged. RNA-seq delivers two novel features that are important for eQTL studies. First, it provides information on allele-specific expression (ASE), which is not available from gene expression microarrays. Second, it generates unprecedentedly rich data to study RNA-Isoform expression. In this paper, we review current methods for eQTL mapping using ASE and discuss some future directions. We also review existing works that use RNA-seq data to study RNA-Isoform expression and we discuss the gaps between these works and Isoform-specific eQTL mapping.
Helga Ingimundardottir - One of the best experts on this subject based on the ideXlab platform.
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Insights into imprinting from parent-of-origin phased methylomes and transcriptomes
Nature Genetics, 2018Co-Authors: Florian Zink, Droplaug N Magnusdottir, Olafur T. Magnusson, Nicolas J Walker, Tiffany J Morris, Asgeir Sigurdsson, Gisli H. Halldorsson, Sigurjon A. Gudjonsson, Páll Melsted, Helga IngimundardottirAbstract:Two hundred and eighty-five methylomes and 11,617 transcriptomes from peripheral blood samples with parent-of-origin-phased haplotypes produce a new map of imprinted methylation and gene expression patterns across the human genome. Imprinting is the preferential expression of one parental allele over the other. It is controlled primarily through differential methylation of cytosine at CpG dinucleotides. Here we combine 285 methylomes and 11,617 transcriptomes from peripheral blood samples with parent-of-origin phased haplotypes, to produce a new map of imprinted methylation and gene expression patterns across the human genome. We demonstrate how imprinted methylation is a continuous rather than a binary characteristic. We describe at high resolution the parent-of-origin methylation pattern at the 15q11.2 Prader–Willi/Angelman syndrome locus, with nearly confluent stochastic pateRNAl methylation punctuated by ‘spikes’ of mateRNAl methylation. We find examples of polymorphic imprinted methylation unrelated (at VTRNA2-1 and PARD6G) or related (at CHRNE) to nearby SNP genotypes. We observe RNA Isoform-specific imprinted expression patterns suggestive of a methylation-sensitive transcriptional elongation block. Finally, we gain new insights into parent-of-origin-specific effects on phenotypes at the DLK1/MEG3 and GNAS loci.
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Insights into imprinting from parent-of-origin phased methylomes and transcriptomes.
Nature Genetics, 2018Co-Authors: Florian Zink, Droplaug N Magnusdottir, Olafur T. Magnusson, Nicolas J Walker, Tiffany J Morris, Asgeir Sigurdsson, Gisli H. Halldorsson, Sigurjon A. Gudjonsson, Páll Melsted, Helga IngimundardottirAbstract:Imprinting is the preferential expression of one parental allele over the other. It is controlled primarily through differential methylation of cytosine at CpG dinucleotides. Here we combine 285 methylomes and 11,617 transcriptomes from peripheral blood samples with parent-of-origin phased haplotypes, to produce a new map of imprinted methylation and gene expression patterns across the human genome. We demonstrate how imprinted methylation is a continuous rather than a binary characteristic. We describe at high resolution the parent-of-origin methylation pattern at the 15q11.2 Prader-Willi/Angelman syndrome locus, with nearly confluent stochastic pateRNAl methylation punctuated by 'spikes' of mateRNAl methylation. We find examples of polymorphic imprinted methylation unrelated (at VTRNA2-1 and PARD6G) or related (at CHRNE) to nearby SNP genotypes. We observe RNA Isoform-specific imprinted expression patterns suggestive of a methylation-sensitive transcriptional elongation block. Finally, we gain new insights into parent-of-origin-specific effects on phenotypes at the DLK1/MEG3 and GNAS loci.
Yuya Ogawa - One of the best experts on this subject based on the ideXlab platform.
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crispr cas9 mediated modulation of splicing efficiency reveals short splicing Isoform of xist RNA is sufficient to induce x chromosome inactivation
Nucleic Acids Research, 2018Co-Authors: Minghui Yue, Yuya OgawaAbstract:AlteRNAtive splicing of mRNA precursors results in multiple protein variants from a single gene and is critical for diverse cellular processes and development. Xist encodes a long noncoding RNA which is a central player to induce X-chromosome inactivation in female mammals and has two major splicing variants: long and short Isoforms of Xist RNA. Although a differentiation-specific and a female-specific expression of Xist Isoforms have been reported, the functional role of each Xist RNA Isoform is largely unexplored. Using CRISPR/Cas9-mediated targeted modification of the 5' splice site in Xist intron 7, we create mutant female ES cell lines which dominantly express the long- or short-splicing Isoform of Xist RNA from the inactive X-chromosome (Xi) upon differentiation. Successful execution of CRISPR/Cas-based splicing modulation indicates that our CRISPR/Cas-based targeted modification of splicing sites is a useful approach to study specific Isoforms of a transcript generated by alteRNAtive splicing. Upon differentiation of splicing-mutant Xist female ES cells, we find that both long and short Xist Isoforms can induce X-chromosome inactivation normally during ES cell differentiation, suggesting that the short splicing Isoform of Xist RNA is sufficient to induce X-chromosome inactivation.
Elsa Bernard - One of the best experts on this subject based on the ideXlab platform.
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A convex formulation for joint RNA Isoform detection and quantification from multiple RNA-seq samples
BMC Bioinformatics, 2015Co-Authors: Elsa Bernard, Laurent Jacob, Julien Mairal, Eric Viara, Jean-philippe VertAbstract:Detecting and quantifying Isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. We propose a new method for solving the Isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. Our convex formulation to jointly detect and quantify Isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some Isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop .
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A convex formulation for joint RNA Isoform detection and quantification from multiple RNA-seq samples
BMC Bioinformatics, 2015Co-Authors: Elsa Bernard, Laurent Jacob, Julien Mairal, Eric Viara, Jean-philippe VertAbstract:Background Detecting and quantifying Isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously. Results We propose a new method for solving the Isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. Conclusion Our convex formulation to jointly detect and quantify Isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some Isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop .
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Efficient RNA Isoform identification and quantification from RNA-Seq data with network flows.
Bioinformatics, 2014Co-Authors: Elsa Bernard, Laurent Jacob, Julien Mairal, Jean-philippe VertAbstract:Several state-of-the-art methods for Isoform identification and quantification are based on l1- regularized regression, such as the Lasso. However, explicitly listing the--possibly exponentially-- large set of candidate transcripts is intractable for genes with many exons. For this reason, existing approaches using the l1-penalty are either restricted to genes with few exons, or only run the regression algorithm on a small set of pre-selected Isoforms. We introduce a new technique called FlipFlop which can efficiently tackle the sparse estimation problem on the full set of candidate Isoforms by using network flow optimization. Our technique removes the need of a preselection step, leading to better Isoform identification while keeping a low computational cost. Experiments with synthetic and real RNA-Seq data confirm that our approach is more accurate than alteRNAtive methods and one of the fastest available. Source code is freely available as an R package from the Bioconductor web site (http://www.bioconductor.org/) and more information is available at http://cbio.ensmp.fr/flipflop.
